Finger vein comparison method, computer equipment, and storage medium

ABSTRACT

A finger vein comparison method, a computer equipment, and a storage medium are provided. The finger vein comparison method includes: two finger vein images to be compared are obtained (S10); image channel fusion is performed on the two finger vein images to be compared to obtain a two-channel target finger vein image to be compared (S20); the target finger vein image to be compared is input into a feature extractor, and a feature vector of the target finger vein image to be compared is extracted by the feature extractor (S30); the feature vector of the target finger vein image to be compared is input into a dichotomy classifier to obtain a dichotomy result (S40); and it is determined according to the dichotomy result whether the two finger vein images to be compared come from the same finger (S50).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation under 35 U.S.C. § 120 of PCTApplication No. PCT/CN2019/104331 filed on Sep. 4, 2019, which claimspriority under 35 U.S.C. § 119(a) and/or PCT Article 8 to Chinese PatentApplication No. 201910263866.4 filed on Apr. 3, 2019, the disclosures ofwhich are hereby incorporated by reference in their entireties.

TECHNICAL FIELD

The application relates to the field of artificial intelligence, inparticular to a finger vein comparison method, a computer equipment, anda storage medium.

BACKGROUND

Finger vein recognition technology is a biological recognitiontechnology that performs personal identification using a vein imageobtained by penetrating the finger with near infrared ray. Among allkinds of biological recognition technologies, the finger veinrecognition technology is a technology using internal biologicalcharacteristics that are not visible from the outside for recognition,so it has high anti-counterfeit performance. However, at present, mostof the research on finger vein recognition is still confined to closedset recognition. Because it is impossible to obtain finger vein imagesof all people for network training in reality, such a closed setrecognition network is not applicable to scenarios of finger veincomparison in real life, so accuracy of the finger vein comparison inreal life scenarios is low.

SUMMARY

In view of this, embodiments of the application provide a finger veincomparison method, a computer equipment, and a storage medium.

In a first aspect, the embodiments of the application provide a fingervein comparison method, which includes the following operations:obtaining two finger vein images to be compared, the two finger veinimages to be compared being single-channel images; performing imagechannel fusion on the two finger vein images to be compared to obtain atwo-channel target finger vein image to be compared; inputting thetarget finger vein image to be compared into a feature extractor, andextracting a feature vector of the target finger vein image to becompared by the feature extractor, the feature extractor being trainedby using a two-channel convolutional neural network; inputting thefeature vector of the target finger vein image to be compared into adichotomy classifier to obtain a dichotomy result; and determiningaccording to the dichotomy result whether the two finger vein images tobe compared come from the same finger.

In a second aspect, a computer equipment is provided, which includes amemory, a processor, and a computer readable instruction stored in thememory and capable of running on the processor. The processor, whenexecuting the computer readable instruction, implements the steps of thefinger vein comparison method.

In a third aspect, the embodiments of the application provide anon-transitory computer readable storage medium, which includes acomputer readable instruction. When executed by the processor, thecomputer readable instruction implements the steps of the finger veincomparison method.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate technical solutions in theembodiments of the application, the accompanying drawings of theembodiments are simply introduced below. It is apparent for those ofordinary skill in the art that the accompanying drawings in thefollowing description are only some embodiments of the application, andsome other accompanying drawings may also be obtained according to theseon the premise of not contributing creative effort.

FIG. 1 is a flowchart of a finger vein comparison method according to anembodiment of the application.

FIG. 2 is a schematic diagram of a finger vein comparison deviceaccording to an embodiment of the application.

FIG. 3 is a schematic diagram of computer equipment according to anembodiment of the application.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In order to better understand the technical solutions of theapplication, the embodiments of the application are described in detailbelow in combination with the accompanying drawings.

It should be clear that the described embodiments are only part, ratherthan all, of the embodiments of the application. All other embodimentsobtained by those of ordinary skill in the art based on the embodimentsin the application without creative work shall fall within the scope ofprotection of the application.

Terms used in the embodiments of the application are for the purpose ofdescribing particular embodiments only and are not intended to limit theapplication. Singular forms “a”, “an” and “the” used in the embodimentsof the application and the appended claims of the present disclosure arealso intended to include the plural forms unless the context clearlyindicates otherwise.

It is to be understood that the term “and/or” used in the application isonly the same field that describes the associated object and representsthat three relationships may exist. For example, A and/or B mayrepresent three conditions: i.e., independent existence of A, existenceof both A and B and independent existence of B. In addition, character“/” in the disclosure usually represents that previous and nextassociated objects form an “or” relationship.

It is to be understood that although the terms first, second, third, andthe like may be adopted in the embodiments of the application todescribe various features, but these features should not be limited tothese terms. These terms are only adopted to distinguish the features.For example, without departing from the scope of the embodiments of theapplication, a first feature may also be called a second feature;similarly, a second feature may also be called a first feature.

For example, the term “if” used here may be explained as “while” or“when” or “responsive to determining” or “responsive to detecting”,which depends on the context. Similarly, based on the context, thephrase “if determining” or “if detecting (stated condition or event)”may be explained as “when determining” or “responsive to determining” or“when detecting (stated condition or event)” or “responsive to detecting(stated condition or event)”.

FIG. 1 is a flowchart of a finger vein comparison method according to anembodiment of the application. The finger vein comparison method may beapplied in a finger vein comparison system. The finger vein comparisonsystem may be used to determine whether finger vein images come from thesame finger. The finger vein comparison system may be specificallyapplied to computer equipment. The computer equipment can interact withusers, and includes, but not limited, to a computer, a smart phone, atablet, etc. As shown in FIG. 1, the finger vein comparison methodincludes the following steps.

At S10, obtain two finger vein images to be compared, which aresingle-channel images.

The finger vein image to be compared refers to a finger vein image forfinger vein comparison. It is understandable that the specifications ofthe two finger vein images to be compared are the same. For example, thespecifications of both the two finger vein images to be compared are128*256. When a finger vein collecting device is used to collect fingervein images, the specifications of the collected finger vein images areallowed to be different. In this case, the finger vein images collectedmay be consistent in specifications through size transformation andother processing, so as to carry out finger vein comparison moreaccurately.

The two finger vein images to be compared are single-channel images. Itis understandable that in order to improve the efficiency of finger veincomparison, after the finger vein collecting device is used to collectthe finger vein images, not only the specifications of finger veinimages are unified, but also the collected finger vein images arepreprocessed, so that the preprocessed finger vein images can reflectthe features of the finger vein images with less data. Converting thecollected finger vein image into a single-channel image is a method. Thechannel of the image is related to how it is coded. If the image isdecomposed into three components of Red, Green and Blue (RGB) forrepresentation, there are three channels. If the image is a grayscaleimage, there is one channel Specifically, in the embodiment, thecollected finger vein images may be uniformly converted into grayscaleimages, at this point, the collected finger vein images aresingle-channel images. If the finger vein image to be compared is thesingle-channel image, the comparison efficiency of the finger veinimages can be improved, and a finger vein comparison result can beobtained according to the finger vein image to be compared.

In an embodiment, two single-channel finger vein images to be comparedmay be obtained by preprocessing the finger vein image collected by thefinger vein collecting device.

At S20, perform image channel fusion on the two finger vein images to becompared to obtain a two-channel target finger vein image to becompared.

The fusion of image channels refers to the superposition of imagechannels. For example, a red-channel image, a green-channel image and ablue-channel image are fused to obtain one image containing a redchannel, a green channel, and a blue channel.

In an embodiment, image channel fusion is performed on two finger veinimages to be compared. That is, the two finger vein images to becompared are superposed to obtain one finger vein image including thesingle channel corresponding to each of the two finger vein images to becompared, and this finger vein image is a two-channel image, called atarget finger vein image to be compared. The target finger vein image tobe compared includes the information of the original single channel oftwo finger vein images to be compared, and has a deep feature about thelevel of similarity between the two finger vein images to be comparedafter the fusion of image channels (understandably, the deep featuresabout the level of similarity between the two finger vein images to becompared that the finger vein images to be compared coming from the samefinger have after channel fusion and the finger vein images to becompared coming from different fingers have after channel fusion, aredifferent). It is possible to determine, based on the deep feature,whether two finger vein images to be compared come from the same finger.

Further, S20 in which the two finger vein images to be compared arefused on image channels to obtain a two-channel target finger vein imageto be compared specifically includes the following steps.

At S21, obtain a single-channel pixel matrix of the first finger veinimage to be compared.

At S22, obtain a single-channel pixel matrix of the second finger veinimage to be compared.

Understandably, the image is composed of pixels and may be representedby pixel matrix. Specifically, the single-channel image is representedby a single-channel pixel matrix.

In an embodiment, the single-channel pixel matrix of the first fingervein image to be compared and the single-channel pixel matrix of thesecond finger vein image to be compared are obtained to fuse the imagechannels.

At S23, perform image channel fusion on pixel values of correspondingpositions in the single-channel pixel matrix of the first finger veinimage to be compared and in the single-channel pixel matrix of thesecond finger vein image to be compared to obtain the target finger veinimage to be compared.

Understandably, the fusion of image channels refers to the superpositionof image channels. A pixel includes the position of the pixel on theimage and its value. In the embodiment, the specifications of the twoimages to be compared are the same, so the images should becorrespondingly fused according to the position of the pixel. Afterfusion, the pixels in the two channels are independent of each other,which is equivalent to that an image is represented by the pixels of twochannels.

In S21 to S23, through the fusion of image channels, one image can beused to represent two images to be compared, which is beneficial toimproving the efficiency of finger vein comparison. Moreover, after thefusion of image channels, the target finger vein image to be comparedhas the deep feature about the level of similarity between two fingervein images to be compared, and based on the deep feature, it ispossible to determine whether two finger vein images to be compared comefrom the same finger.

At S30, input the target finger vein image to be compared into a featureextractor, and extract a feature vector of the target finger vein imageto be compared by the feature extractor, the feature extractor beingtrained by using a two-channel convolutional neural network.

The two-channel convolutional neural network is a convolutional neuralnetwork for recognizing a two-channel image, which has a good effect onextracting the feature vector of the two-channel image. The featureextractor trained by the two-channel convolutional neural network canextract the feature vector from the two-channel image. The featurevector can accurately reflect the deep feature of the two-channel image.

In an embodiment, the feature extractor is used to extract the featurevector of the input target finger vein image to be compared. Because thefeature extractor is trained by the two-channel convolutional neuralnetwork, it can accurately extract the feature vector of the targetfinger vein image to be compared, which is helpful to compare the fingervein images to be compared according to the feature vector and obtain anaccurate finger vein comparison result.

Further, before S30, that is, before is input the target finger veinimage to be compared into the feature extractor, the method furtherincludes the following steps.

At S31, obtain a finger vein sample to be trained.

The sample for training and the sample for testing in the finger veinsample(s) to be trained do not overlap with each other and follow thestandard of open set. Understandably, at present, finger veinrecognition basically stays in a closed set, and all the samples usedfor testing have appeared in a training set. In this case, a trainedmodel will overfit. The accuracy rate for the finger vein comparison ofthe sample in the closed set is high, but the accuracy rate for thecomparison in real life scenarios (under the open set condition) is low,which cannot meet the requirements of normal finger vein comparison. Inreal life scenarios, there are few data sets about the finger veinimages, but the standard of open set is still followed when a featureextraction model is trained, so that the samples used for training andthe samples used for testing do not overlap with each other, which caneffectively improve the generalization ability of the model.

At S32, input the finger vein sample to be trained into the two-channelconvolutional neural network for training, and use an enhanced edgecosine loss function to calculate a loss value generated in the trainingprocess. The two-channel convolutional neural network includes fourconvolution layers, four pooling layers, and one fully connected layer.The first layer, the third layer, the fifth layer, and the seventh layerof the two-channel convolutional neural network are the convolutionlayers; the second layer, the fourth layer, the sixth layer, and theeighth layer are the pooling layers; and the ninth layer is the fullyconnected layer.

In the embodiment, calculating the loss value generated in the modeltraining process specifically uses the enhanced edge cosine lossfunction. The enhanced edge cosine loss function takes a normalizedfeature vector as the input (the two-channel convolutional neuralnetwork extracts and normalizes the feature vector of the input fingervein sample to be trained), and can further maximize the decisionboundary of the learned deep feature in a cosine angular space and learnthe highly discriminative deep feature by maximizing an interclasscosine edge.

In the embodiment, the network structure specifically adopted by thetwo-channel convolutional neural network is only four convolutionlayers, four pooling layers, and one fully connected layer. The firstlayer, the third layer, the fifth layer, and the seventh layer of thetwo-channel convolutional neural network are the convolution layers; thesecond layer, the fourth layer, the sixth layer, and the eighth layerare the pooling layers; and the ninth layer is the fully connectedlayer. The network structure is simple, on the premise of accuratelyextracting the feature vector of the finger vein sample to be trained,the efficiency of model training is effectively improved, and thereal-time performance of finger vein comparison can be guaranteed.

At S33, update the two-channel convolutional neural network according tothe loss value to obtain the feature extractor.

The loss value reflects the value of deviation between the output resultof training and the actual expectation. A back propagation algorithm maybe used to iterate and update network parameters in the two-channelconvolutional neural network according to the loss value, so that theoutput result after each network update is closer to the actualexpectation. When the training reaches the preset number of iterationsor all change values of the network parameters are less than aniteration stopping threshold, the training ends and the featureextraction model is obtained. The feature extraction model is trained toextract the feature vector of the two-channel image, and can extract thefeature vector of the target finger vein image to be compared in realtime and accurately.

In S31 to S33, a specific implementation mode of training to obtain thefeature extraction model is provided. The training process follows thestandard of open set and can improve the generalization ability of themodel. Moreover, the two-channel convolutional neural network fortraining uses the enhanced edge cosine loss function to calculate theloss value generated in the training process, and the two-channelconvolutional neural network is iterated and updated to obtain thefeature extraction model. In this way, the loss generated in thetraining process can be described more accurately, and it is helpful toimprove the efficiency of model training and the feature extractionability of the feature extraction model.

Further, S31 in which the finger vein sample to be trained is obtainedspecifically includes the following steps.

At S311, obtain single-channel finger vein images to be trained.

Understandably, if the finger vein image initially collected by thefinger vein collecting device is not the single-channel image, theinitially collected finger vein image is converted, according to thespecified image preprocessing, into a single-channel finger vein imageto be trained with uniform standards.

At S312, perform image channel fusion on every two single-channel fingervein images to be trained to obtain a two-channel finger vein image tobe trained.

In real life scenarios, there are relatively few data sets about thefinger vein image. In the embodiment, by using the fusion processingmethod of image channel, every two single-channel finger vein images tobe trained are fused into a two-channel finger vein image to be trained,and n single-channel finger vein images to be trained are increased ton*(n−1)/2 two-channel finger vein images to be trained, so that the dataset of the finger vein image can be more fully utilized.

At S313, if the single-channel finger vein images to be trained forfusing come from the same finger, set a label value of the two-channelfinger vein image to be trained to 0.

At S314, if the single-channel finger vein images to be trained forfusing do not come from the same finger, set the label value of thetwo-channel finger vein image to be trained to 1.

In an embodiment, it is needed to mark the obtained two-channel fingervein image to be trained in advance, so as to effectively update thenetwork parameters through the marked label values in the process oftraining the feature extraction model. The comparison of finger veinimages is a dichotomy problem, so the label value may be set accordingto whether the single-channel finger vein images to be trained forfusing come from the same finger.

At S315, obtain the finger vein sample to be trained according to theset label value.

Understandably, after the label value of a two-channel finger veinsample to be trained is set, the finger vein sample to be trained forthe training feature extraction model is obtained.

In S311 to S315, a specific implementation mode of obtaining the fingervein sample to be trained is provided; by using the fusion processingmethod of image channel, every two single-channel finger vein images tobe trained are fused into a two-channel finger vein image to be trained,and n single-channel finger vein images to be trained are increased ton*(n−1)/2 two-channel finger vein images to be trained, so that the dataset of the finger vein image can be more fully utilized; and the labelvalue is assigned according to whether the single-channel finger veinimages to be trained for fusing come from the same finger, which ishelpful to effectively adjust the network parameters in the process oftraining the feature extraction model.

At S40, input the feature vector of the target finger vein image to becompared into a dichotomy classifier to obtain a dichotomy result.

The dichotomy classifier may be a classifier pre-trained by using theneural network, specifically a Support Vector Machine (SVM) dichotomyclassifier. The SVM dichotomy classifier can output two classificationresults according to the input feature vector. Specifically, the inputfeature vector may be the feature vector of the target finger vein imageto be compared, and the classification result may be the classificationresult about whether two finger vein images to be compared come from thesame finger.

In an embodiment, the feature vector of the target finger vein image tobe compared is input into the dichotomy classifier, and the dichotomyresult is output according to whether two finger vein images to becompared come from the same finger.

At S50, determine according to the dichotomy result whether the twofinger vein images to be compared come from the same finger.

Further, S50 in which it is determined according to the dichotomy resultwhether the two finger vein images to be compared come from the samefinger specifically includes the following steps.

At S51, if a value of the dichotomy result is 0, determine that the twofinger vein images to be compared come from the same finger.

At S51, if the value of the dichotomy result is 1, determine that thetwo finger vein images to be compared do not come from the same finger.

Understandably, the dichotomy result being 0 means that two finger veinimages to be compared come from the same finger, and the dichotomyresult being 1 means that two finger vein images to be compared do notcome from the same finger.

In S51 to S52, a specific implementation mode of determining, accordingto the dichotomy result, whether two finger vein images to be comparedcome from the same finger is provided, which can quickly obtain anaccurate finger vein comparison result.

In the embodiments of the application, first of all, two finger veinimages to be compared of single-channel images are obtained, and imagechannel fusion is performed on the two finger vein images to be comparedto obtain a two-channel target finger vein image to be compared; oneimage can be used to represent two finger vein images to be comparedthrough the fusion of image channels, and has a deep feature about thelevel of similarity between the two finger vein images to be comparedafter the fusion of image channels; based on the deep feature, it ispossible to determine whether the two finger vein images to be comparedcome from the same finger; then, the target finger vein image to becompared is input into the feature extractor, and the feature vector ofthe target finger vein image to be compared is extracted by the featureextractor; the extracted feature vector can reflect the deep feature ofthe target finger vein image to be compared, and based on the featurevector, it is helpful to compare the finger vein image to be comparedand obtain an accurate finger vein comparison result; at last, thefeature vector of the target finger vein image to be compared is inputinto the dichotomy classifier to obtain the dichotomy result, and it isdetermined according to the dichotomy result whether the finger veinimages to be compared come from the same finger. From the perspective ofthe deep feature of the finger vein image to be compared, the featurevector of the target finger vein image to be compared can be accuratelydistinguished by using the dichotomy classifier, and an accurate fingervein comparison result can be obtained.

It should be understood that, in the above embodiments, a magnitude of asequence number of each step does not mean an execution sequence, andthe execution sequence of each process should be determined by itsfunction and an internal logic and should not form any limit to animplementation process of the embodiments of the disclosure.

Based on the finger vein comparison method provided in the embodiment,the application further provides a device embodiment for implementingeach step in the method embodiment and the method.

FIG. 2 is a functional block diagram of a finger vein comparison devicecorresponding to the finger vein comparison method in the embodiment. Asshown in FIG. 2, the finger vein comparison device includes: a firstobtaining module 10, a second obtaining module 20, a third obtainingmodule 30, a fourth obtaining module 40, and a determining module 50.The functions realized by the first obtaining module 10, the secondobtaining module 20, the third obtaining module 30, the fourth obtainingmodule 40, and the determining module 50 correspond to the steps of thefinger vein comparison method in the embodiment, and will not bedescribed in detail here for avoiding redundant elaboration.

The first obtaining module 10 is configured to obtain two finger veinimages to be compared, which are single-channel images. The secondobtaining module 20 is configured to fuse the two finger vein images tobe compared on image channels to obtain the two-channel target fingervein image to be compared. The third obtaining module 30 is configuredto input the target finger vein image to be compared into the featureextractor, and extract the feature vector of the target finger veinimage to be compared by the feature extractor, the feature extractorbeing obtained by using the two-channel convolutional neural network totrain. The fourth obtaining module 40 is configured to input the featurevector of the target finger vein image to be compared into the dichotomyclassifier to obtain the dichotomy result. The determining module 50 isconfigured to determine according to the dichotomy result whether thetwo finger vein images to be compared come from the same finger.

Optionally, the second obtaining module 20 includes a first obtainingunit, a second obtaining unit, and a third obtaining unit. The firstobtaining unit is configured to obtain the single-channel pixel matrixof the first finger vein image to be compared. The second obtaining unitis configured to obtain the single-channel pixel matrix of the secondfinger vein image to be compared. The third obtaining unit is configuredto fuse the pixel values of corresponding positions in thesingle-channel pixel matrix of the first finger vein image to becompared and in the single-channel pixel matrix of the second fingervein image to be compared on image channels to obtain the target fingervein image to be compared.

Optionally, the finger vein comparison device further includes a fourthobtaining unit, a loss value obtaining unit, and a feature extractorobtaining unit. The fourth obtaining unit is configured to obtain thefinger vein sample to be trained. The loss value obtaining unit isconfigured to input the finger vein sample to be trained into thetwo-channel convolutional neural network for training, and use theenhanced edge cosine loss function to calculate the loss value generatedin the training process. The two-channel convolutional neural networkincludes four convolution layers, four pooling layers, and one fullyconnected layer. The first layer, the third layer, the fifth layer, andthe seventh layer of the two-channel convolutional neural network arethe convolution layers; the second layer, the fourth layer, the sixthlayer, and the eighth layer are the pooling layers; and the ninth layeris the fully connected layer. The feature extractor obtaining unit isconfigured to update the two-channel convolutional neural networkaccording to the loss value to obtain the feature extractor.

Optionally, the fourth obtaining unit includes a first obtainingsubunit, a second obtaining subunit, a third obtaining subunit, a fourthobtaining subunit, and a fifth obtaining subunit. The first obtainingsubunit is configured to obtain the single-channel finger vein image tobe trained. The second obtaining subunit is configured to fuse every twosingle-channel finger vein images to be trained on image channels toobtain the two-channel finger vein image to be trained. The thirdobtaining subunit is configured to, when the single-channel finger veinimages to be trained for fusing come from the same finger, set the labelvalue of the two-channel finger vein image to be trained to 0. Thefourth obtaining subunit is configured to, when the single-channelfinger vein images to be trained do not for fusing come from the samefinger, set the label value of the two-channel finger vein image to betrained to 1. The fifth obtaining subunit is configured to obtain thefinger vein sample to be trained according to the set label value.

Optionally, the determining module 50 includes a first determining unitand a second determining unit. The first determining unit is configuredto, when a value of the dichotomy result is 0, determine that the twofinger vein images to be compared come from the same finger. The seconddetermining unit is configured to, when the value of the dichotomyresult is 1, determine that the two finger vein images to be compared donot come from the same finger.

In the embodiments of the application, first of all, two finger veinimages to be compared of single-channel images are obtained, and imagechannel fusion is performed on the two finger vein images to be comparedto obtain a two-channel target finger vein image to be compared; oneimage can be used to represent two finger vein images to be comparedthrough the fusion of image channels, and has a deep feature about thelevel of similarity between the two finger vein images to be comparedafter the fusion of image channels; based on the deep feature, it ispossible to determine whether the two finger vein images to be comparedcome from the same finger; then, the target finger vein image to becompared is input into the feature extractor, and the feature vector ofthe target finger vein image to be compared is extracted by the featureextractor; the extracted feature vector can reflect the deep feature ofthe target finger vein image to be compared, and based on the featurevector, it is helpful to compare the finger vein image to be comparedand obtain an accurate finger vein comparison result; at last, thefeature vector of the target finger vein image to be compared is inputinto the dichotomy classifier to obtain the dichotomy result, and it isdetermined according to the dichotomy result whether the finger veinimages to be compared come from the same finger. From the perspective ofthe deep feature of the finger vein image to be compared, the featurevector of the target finger vein image to be compared can be accuratelydistinguished by using the dichotomy classifier, and an accurate fingervein comparison result can be obtained.

The embodiment provides a non-transitory computer readable storagemedium, in which a computer readable instruction is stored. The computerreadable instruction, when executed by a processor, implements thefinger vein comparison method, which will not be repeated here foravoiding repetition. Or, the computer readable instruction, whenexecuted by the processor, realizes the function of each module/unit inthe finger vein comparison device, which will not be repeated here foravoiding repetition.

FIG. 3 is a schematic diagram of a computer equipment provided in anembodiment of the application. As shown in FIG. 3, the computerequipment 60 in the embodiment includes: a processor 61, a memory 62,and a computer readable instruction 63 stored in the memory 62 andcapable of running on the processor 61. The computer readableinstruction 63, when executed by the processor 61, implements the fingervein comparison method in the embodiment, which will not be repeatedhere for avoiding repetition. Or, the computer readable instruction 63,when executed by the processor 61, realizes the function of eachmodule/unit in the finger vein comparison device, which will not berepeated here for avoiding repetition.

The computer equipment 60 may be a desktop computer, a laptop, a PDA, acloud server, and so on. The computer equipment 60 may include, but notlimited to, the processor 61 and the memory 62. Those of ordinary skillin the art may understand that the FIG. 3 is only an example of thecomputer equipment 60 and does not form a limit to the computerequipment 60. The server may include more or fewer parts than shown, orsome combination of parts, or different parts. For example, the computerequipment may further include an input/output device, a network accessdevice, a bus, etc.

The processor 61 may be a Central Processing Unit (CPU), and may also beother universal processor, a Digital Signal Processor (DSP), an ASIC, anFPGA or other programmable logic devices, discrete gates or transistorlogic devices, and discrete hardware component, etc. The universalprocessor may be a microprocessor, or the processor may also be anyconventional processor and the like.

The memory 62 may be an internal storage unit of the computer equipment60, such as a hard disk or memory of the computer equipment 60. Thememory 62 may also be an external storage device of the computerequipment 60, such as a plug-in hard disk, a Smart Media Card (SMC), aSecure Digital (SD) card, and a flash card, provided on the computerequipment 60. Further, the memory 62 may further include both theinternal storage unit and the external storage device of the computerequipment 60. The memory 62 is used to store the computer readableinstruction and other programs and data needed by the computerequipment. The memory 62 may also be used for temporarily store datathat has been or will be output.

Those of ordinary skill in the art may clearly understand that, forconvenience and simplicity of description, illustration is given onlybased on the division of the above functional units and modules. In thepractical applications, the above functions may be allocated todifferent functional units and modules for realization according toneeds, that is, the internal structure of the device is divided intodifferent functional units or modules to realize all or part of thefunctions described above.

The above embodiments are only used for illustrating, but not limiting,the technical solutions of the disclosure. Although the disclosure iselaborated referring to the above embodiments, those of ordinary skillin the art should understand that they may still modify the technicalsolutions in each above embodiment, or equivalently replace a part oftechnical features; but these modifications and replacements do not makethe nature of the corresponding technical solutions depart from thespirit and scope of the technical solutions in each embodiment of thedisclosure, and these modifications and replacements should be includedin the scope of protection of the disclosure.

What is claimed is:
 1. A finger vein comparison method, comprising:obtaining two finger vein images to be compared, wherein the two fingervein images to be compared are single-channel images; performing imagechannel fusion on the two finger vein images to be compared to obtain atwo-channel target finger vein image to be compared; inputting thetarget finger vein image to be compared into a feature extractor, andextracting a feature vector of the target finger vein image to becompared by the feature extractor, wherein the feature extractor istrained by using a two-channel convolutional neural network; inputtingthe feature vector of the target finger vein image to be compared into adichotomy classifier to obtain a dichotomy result; and determiningaccording to the dichotomy result whether the two finger vein images tobe compared come from a same finger.
 2. The method as claimed in claim1, wherein performing image channel fusion on the two finger vein imagesto be compared to obtain the two-channel target finger vein image to becompared comprises: obtaining a single-channel pixel matrix of a firstfinger vein image to be compared; obtaining a single-channel pixelmatrix of a second finger vein image to be compared; and performingimage channel fusion on pixel values of corresponding positions in thesingle-channel pixel matrix of the first finger vein image to becompared and in the single-channel pixel matrix of the second fingervein image to be compared, to obtain the target finger vein image to becompared.
 3. The method as claimed in claim 1, further comprising beforeinputting the target finger vein image to be compared into the featureextractor: obtaining a finger vein sample to be trained; inputting thefinger vein sample to be trained into the two-channel convolutionalneural network for a training process, and using an enhanced edge cosineloss function to calculate a loss value generated in the trainingprocess, wherein the two-channel convolutional neural network comprisesfour convolution layers, four pooling layers, and one fully connectedlayer, wherein the first layer, the third layer, the fifth layer, andthe seventh layer of the two-channel convolutional neural network arethe convolution layers, wherein the second layer, the fourth layer, thesixth layer, and the eighth layer are the pooling layers, and whereinthe ninth layer is the fully connected layer; and updating thetwo-channel convolutional neural network according to the loss value toobtain the feature extractor.
 4. The method as claimed in claim 3,wherein obtaining the finger vein sample to be trained comprises:obtaining single-channel finger vein images to be trained; performingimage channel fusion on every two single-channel finger vein images tobe trained to obtain a two-channel finger vein image to be trained; whenthe single-channel finger vein images to be trained for fusing come fromthe same finger, setting a label value of the two-channel finger veinimage to be trained to 0; when the single-channel finger vein images tobe trained for fusing do not come from the same finger, setting thelabel value of the two-channel finger vein image to be trained to 1; andobtaining the finger vein sample to be trained according to the setlabel value.
 5. The method as claimed in claim 1, wherein determiningaccording to the dichotomy result whether the two finger vein images tobe compared come from the same finger comprises: when a value of thedichotomy result is 0, determining that the two finger vein images to becompared come from the same finger; and when the value of the dichotomyresult is 1, determining that the two finger vein images to be compareddo not come from the same finger.
 6. The method as claimed in claim 2,wherein determining according to the dichotomy result whether the twofinger vein images to be compared come from the same finger comprises:when a value of the dichotomy result is 0, determining that the twofinger vein images to be compared come from the same finger; and whenthe value of the dichotomy result is 1, determining that the two fingervein images to be compared do not come from the same finger.
 7. Themethod as claimed in claim 3, wherein determining according to thedichotomy result whether the two finger vein images to be compared comefrom the same finger comprises: when a value of the dichotomy result is0, determining that the two finger vein images to be compared come fromthe same finger; and when the value of the dichotomy result is 1,determining that the two finger vein images to be compared do not comefrom the same finger.
 8. Computer equipment, comprising: a memory, aprocessor, and a computer readable instruction stored in the memory andcapable of running on the processor, wherein the processor, whenexecuting the computer readable instruction, implements: obtaining twofinger vein images to be compared, wherein the two finger vein imagesare single-channel images; performing image channel fusion on the twofinger vein images to be compared to obtain a two-channel target fingervein image to be compared; inputting the target finger vein image to becompared into a feature extractor, and extracting a feature vector ofthe target finger vein image to be compared by the feature extractor,wherein the feature extractor is trained by using a two-channelconvolutional neural network; inputting the feature vector of the targetfinger vein image to be compared into a dichotomy classifier to obtain adichotomy result; and determining according to the dichotomy resultwhether the two finger vein images to be compared come from a samefinger.
 9. The computer equipment as claimed in claim 8, wherein toimplement performing image channel fusion on the two finger vein imagesto be compared to obtain the two-channel target finger vein image to becompared, the processor executes the computer readable instruction toimplement: obtaining a single-channel pixel matrix of a first fingervein image to be compared; obtaining a single-channel pixel matrix of asecond finger vein image to be compared; and performing image channelfusion on pixel values of corresponding positions in the single-channelpixel matrix of the first finger vein image to be compared and in thesingle-channel pixel matrix of the second finger vein image to becompared, to obtain the target finger vein image to be compared.
 10. Thecomputer equipment as claimed in claim 8, wherein the processor, whenexecuting the computer readable instruction, further implements: beforeinputting the target finger vein image to be compared into the featureextractor, obtaining a finger vein sample to be trained; inputting thefinger vein sample to be trained into the two-channel convolutionalneural network for a training process, and using an enhanced edge cosineloss function to calculate a loss value generated in the trainingprocess, wherein the two-channel convolutional neural network comprisesfour convolution layers, four pooling layers, and one fully connectedlayer, wherein the first layer, the third layer, the fifth layer, andthe seventh layer of the two-channel convolutional neural network arethe convolution layers, wherein the second layer, the fourth layer, thesixth layer, and the eighth layer are the pooling layers, and whereinthe ninth layer is the fully connected layer; and updating thetwo-channel convolutional neural network according to the loss value toobtain the feature extractor.
 11. The computer equipment as claimed inclaim 10, wherein to implement obtaining the finger vein sample to betrained, the processor executes the computer readable instruction toimplement: obtaining single-channel finger vein images to be trained;performing image channel fusion on every two single-channel finger veinimages to be trained to obtain a two-channel finger vein image to betrained; when the single-channel finger vein images to be trained forfusing come from the same finger, setting a label value of thetwo-channel finger vein image to be trained to 0; when thesingle-channel finger vein images to be trained for fusing do not comefrom the same finger, setting the label value of the two-channel fingervein image to be trained to 1; and obtaining the finger vein sample tobe trained according to the set label value.
 12. The computer equipmentas claimed in claim 8, wherein to implement determining according to thedichotomy result whether the two finger vein images to be compared comefrom the same finger, the processor executes the computer readableinstruction to implement: when a value of the dichotomy result is 0,determining that the two finger vein images to be compared come from thesame finger; and when the value of the dichotomy result is 1,determining that the two finger vein images to be compared do not comefrom the same finger.
 13. The computer equipment as claimed in claim 9,wherein to implement determining according to the dichotomy resultwhether the two finger vein images to be compared come from the samefinger, the processor executes the computer readable instruction toimplement: when a value of the dichotomy result is 0, determining thatthe two finger vein images to be compared come from the same finger; andwhen the value of the dichotomy result is 1, determining that the twofinger vein images to be compared do not come from the same finger. 14.The computer equipment as claimed in claim 10, wherein to implementdetermining according to the dichotomy result whether the two fingervein images to be compared come from the same finger, the processorexecutes the computer readable instruction to implement: when a value ofthe dichotomy result is 0, determining that the two finger vein imagesto be compared come from the same finger; and when the value of thedichotomy result is 1, determining that the two finger vein images to becompared do not come from the same finger.
 15. A non-transitory computerreadable storage medium that stores a computer readable instruction,wherein the computer readable instruction, when executed by a processor,enables the processor to implement: obtaining two finger vein images tobe compared are obtained, wherein the two finger vein images aresingle-channel images; performing image channel fusion on the two fingervein images to be compared to obtain a two-channel target finger veinimage to be compared; inputting the target finger vein image to becompared into a feature extractor, and extracting a feature vector ofthe target finger vein image to be compared by the feature extractor,wherein the feature extractor is trained by using a two-channelconvolutional neural network; inputting the feature vector of the targetfinger vein image to be compared into a dichotomy classifier to obtain adichotomy result; and determining according to the dichotomy resultwhether the two finger vein images to be compared come from a samefinger.
 16. The non-transitory computer readable storage medium asclaimed in claim 15, to implement performing image channel fusion on thetwo finger vein images to be compared to obtain the two-channel targetfinger vein image to be compared, the computer readable instruction isexecuted by the processor to implement: obtaining a single-channel pixelmatrix of a first finger vein image to be compared; obtaining asingle-channel pixel matrix of a second finger vein image to becompared; and performing image channel fusion on pixel values ofcorresponding positions in the single-channel pixel matrix of the firstfinger vein image to be compared and in the single-channel pixel matrixof the second finger vein image to be compared, to obtain the targetfinger vein image to be compared.
 17. The non-transitory computerreadable storage medium as claimed in claim 15, wherein the computerreadable instruction, when executed by the processor, enables theprocessor to further implement: before inputting the target finger veinimage to be compared into the feature extractor, obtaining a finger veinsample to be trained; inputting the finger vein sample to be trainedinto the two-channel convolutional neural network for a trainingprocess, and using an enhanced edge cosine loss function to calculate aloss value generated in the training process, wherein the two-channelconvolutional neural network comprises four convolution layers, fourpooling layers, and one fully connected layer, wherein the first layer,the third layer, the fifth layer, and the seventh layer of thetwo-channel convolutional neural network are the convolution layers,wherein the second layer, the fourth layer, the sixth layer, and theeighth layer are the pooling layers, and wherein the ninth layer is thefully connected layer; and updating the two-channel convolutional neuralnetwork according to the loss value to obtain the feature extractor. 18.The non-transitory computer readable storage medium as claimed in claim17, wherein to implement obtaining the finger vein sample to be trained,the computer readable instruction is executed by the processor toimplement: obtaining single-channel finger vein images to be trained;performing image channel fusion on every two single-channel finger veinimages to be trained to obtain a two-channel finger vein image to betrained; when the single-channel finger vein images to be trained forfusing come from the same finger, setting a label value of thetwo-channel finger vein image to be trained to 0; when thesingle-channel finger vein images to be trained for fusing do not comefrom the same finger, setting the label value of the two-channel fingervein image to be trained to 1; and obtaining the finger vein sample tobe trained according to the set label value.
 19. The non-transitorycomputer readable storage medium as claimed in claim 15, wherein toimplement determining according to the dichotomy result whether the twofinger vein images to be compared come from the same finger, thecomputer readable instruction is executed by the processor to implement:when a value of the dichotomy result is 0, determining that the twofinger vein images to be compared come from the same finger; and whenthe value of the dichotomy result is 1, determining that the two fingervein images to be compared do not come from the same finger.
 20. Thenon-transitory computer readable storage medium as claimed in claim 16,wherein to implement determining according to the dichotomy resultwhether the two finger vein images to be compared come from the samefinger, the computer readable instruction is executed by the processorto implement: when a value of the dichotomy result is 0, determiningthat the two finger vein images to be compared come from the samefinger; and when the value of the dichotomy result is 1, determiningthat the two finger vein images to be compared do not come from the samefinger.